Research and KTH Geoinformatics. Yifang Ban, Professor

Transcription

1 KTH ROYAL INSTITUTE OF TECHNOLOGY Research and KTH Geoinformatics Yifang Ban, Professor Director, Division of Geoinformatics Vice Chair, Department for Urban Planning and Environment KTH Royal Institute of Technology Introduction Ø 18 staff (with 4 part-time) in full professor, 2 affiliated professors (HiG) - 2 associate professor & 1 affiliated associated professor - 2 postdoc. researcher & 2 research engineer - 10 (4 part-time) PhD students Ø International faculty and Ph.D. students from Sweden, Austria, Canada, China, Germany, Iran, Japan, Rwanda, Sudan, U. K. & U.S. Ø Young and energetic research team based on solid financing with substantial external funding 1

2 Education Ø 12 Courses ranging from students Ø S-Program Geomatics Engineering Profile Ø International M.Sc. Program in Transport & Geoinformation Technology Ø Developing courses for other programs - Real Estate Information Technology - Aerospace Engineering Program Research Themes Multisensor Remote Sensing & Digital Image Analysis Multisensor Image Processing & Data Fusion Change Detection Object-Based & Knowledge-Based Image Classification Visualization, Analysis, Management and Modelling of Geospatial Data Visualization (3-5D) of Geospatial Data Spatial Data Mining, Network Analysis, Spatial Databases Agent-Based Modelling GeoIT Applications Urban, Landuse/Land-Cover Mapping & Change Detection Environmental Impact Assessment of Landscape Change Transport Analysis Scenario-based Urban Growth Modelling Epidemiology Geographic Analysis of Cardiac Arrest 2

3 Current Projects ESA DUE INNOVATOR III: EO4Urban, Sentinel4Urban, PI: Yifang Ban, funded by the Swedish National Space Board, , 4.25 Mkr EO4SmartCities, ESA/MOST China Dragon 4 Program, IntegrCity, EU, Remote Sensing & GIS Research and Education at Univ. of Rwanda, & Makerere Univ., Uganda Rwanda, (15 Million kr in total, KTH share: 4.5Mkr). Uganda, , (6 M kr in totoal, KTH share: 1Mkr) Center for Cardiac Arrest Science, Hjärt-Lungfondens Stora forskningsanslag, PI: Prof. Leif Svensson, KI, Yifang Ban as Co-I (15 Mkr in total). Shanghai,

4 Shanghai, 2010 Urbanization in Stockholm 4

5 Global Urbanization Ø In 2008, more than 50% of the world population live in cities. Ø By 2050, the world is expected to add an additional 2.5 billion urban dwellers; Ø with nearly 90 percent of the increase concentrated in Asia and Africa. (United Nations, 2014). Megacities 2015 Mumbai Delhi Dhaka Tokyo Mexico City New York Paris Istanbul Moscow Cairo Karachi Kolkata Seoul Beijing Osaka Tianjin Jakarta Los Angeles Sao Paulo London Rhine-Ruhr Kabul Lahore Shanghai Chicago Baghdad Ho Chi Minh City Tehran Jeddah Surat Yangon Bangkok Bandung Toronto Rio de Janeiro Bogotá Abidjan Lagos Luanda Guatemala City Belo Horizonte Lima Population worldwide in cities Santiago Buenos Aires % Source: U.N. Population Division % % 5

6 Environmental Consequences Ø High concentrations of aerosols, exhaust gases, pollution and dust Hazardous to health Increased smog, haze, fog, clouds Source: The Associated Press Source: Suicup via Wikimedia Source: zmescience.com Environmental Consequences Ø Paved surfaces -> rainfall water -> flooding Urbanization results in more impervious surfaces, thus reducing the area where infiltration to ground water can occur. Thus, more storm water runoff occurs. Poor storm water systems are unable to prevent flooding 79 people died in July 2012 Beijing flooding Source: rendezvous.blogs.nytimes.com Source: BBC News Source: 6

7 Environmental Consequences Ø Urban heat island (UHI) and heat waves UHI urban air temperatures higher than surrounding rural areas. The average air temperature in a city with 1 million inhabitants is 1-3 degrees warmer. Heat waves: In the afternoon, the difference can be 12 degrees warmer, no night time cooling. Death rate raise during heat waves. Satellite Data 7

8 EO4Urban: Objectives Ø The overall objective is to evaluate multi-temporal multi-resolution Sentinel-1A SAR and Sentinel-2A MSI data for developing a pilot global urban services based on user requirements to support smart and sustainable urban development. Team KTH Royal Institute of Technology, Sweden University of Pavia, Italy Users Stockholm County Administrative Board, Sweden National Geomatics Center, China User Requirement Baseline Ø Global Urban Mapping - Urban Extent Maps Ø Semi-Global Urban Mapping - Urban Land Cover Maps Ø City-Level Urban Mapping - New Builtup Area Maps - Urban Green Structure Maps - Urban Green Structure Change Maps 8

9 User Requirements: Urban Extent Maps Ø 2015 and 2016 Urban extent maps for Stockholm and Beijing Ø Minimum Mapping Unit at 30m x 30m. Ø Historical urban extent maps from 1995, 2005 and 2010 if possible. User Requirements: Urban Land Cover Maps High-Density Builtup Areas: Large buildings and Builtup areas with >= 50% buildings MMU: 30mx30m (3 pixels x 3 pixels for Sentinel-2A MSI data), acceptable accuracy: 80% Low-Density Builtup Areas: Villas with gardens and builtup areas with < 50% buildings MMU: 30mx30m (3 pixels x 3 pixels for Sentinel-2A MSI data), acceptable accuracy: 80% Roads, Railways and Airport Runways: Highways, local roads, railways, airport runways MMU: 10mx30m (1 pixels x 3 pixels for Sentinel-2A MSI data), acceptable accuracy: 60% Urban Green Structure: Parks, botanical gardens, zoos, playgrounds, sports fields, colony lots, cemeteries and grass areas MMU: 30mx30m (3 pixels x 3 pixels for Sentinel-2A MSI data), acceptable accuracy: 80% Golf Courses: golf courses, MMU: 30mx30m (3 pixels x 3 pixels for Sentinel-2A MSI data), acceptable accuracy: 80% Agricultural Fields: Vegetated and bare agricultural fields MMU: 60mx60m (6 pixels x 6 pixels for Sentinel-2A MSI data), acceptable accuracy: 80% Forest: includes Conifers, deciduous and mixed forest. MMU: 60m x 60m (6 pixels x 6 pixels for Sentinel-2A MSI data), acceptable accuracy: 80% Water: Rivers and Water Bodies MMU: Rivers: 10m x 30m and Water Bodies: 30m x 30m (1 pixels x 3 pixels or 3 pixels x 3 pixels for Sentinel-2A MSI data), acceptable accuracy: 95% 9

10 User Requirements: New Builtup Area Maps Ø Maps of new built-up areas: base map in 2015 and updated yearly Ø Minimum Mapping Unit at 30m x 30m. User Requirements: Urban Green Structure Maps Ø Maps of Urban Green Structure in 2015 and updated yearly Ø Minimum Mapping Unit at 30m x 30m. 10

11 User Requirements: Urban Green Structure Change Maps Ø Maps of urban green structure changes in 2015 and updated yearly Ø Minimum Mapping Unit at 30m x 30m. Study Areas 11

12 Sentinel-1A SAR Data 2015/05/16 ASC 2015/06/04 DSC 2015/06/09 ASC 2015/06/28 DSC 2015/07/10 DSC 2015/07/15 ASC 2015/07/22 DSC 2015/08/08 ASC 2015/08/15 DSC 2015/08/20 ASC 2015/09/08 DSC 2015/09/20 DSC 2015/09/25 ASC 2015/10/02 DSC 2015/10/07 ASC 2015/10/19 ASC 2015/10/26 DSC 2015/10/31 ASC EO4URBAN KICKOFF (FRASCATI, ITALY) Sentinel-2A MSI Data EO4URBAN KICKOFF (FRASCATI, ITALY)

13 Sentinel-2A MSI Data Sentinel-2A: Pan-sharpening 13

14 EO4Urban 27 EO4Urban: KTH-Pavia Urban Extractor 28 14

15 EO4Urban: KTH-SEG Ban, Y. and A. Jacob, Object-based Fusion of Multitemporal Multi-angle ENVISAT ASAR and HJ-1 Multispectral Data for Urban Land-Cover Mapping. IEEE Transaction on GeoScience and Remote Sensing, Vol. 51, No. 4, pp Stockholm Urban Extraction Using KTH-Pavia Urban Extractor Results: Stockholm 15

16 Urban Extraction: ENVISAT ASAR Data Beijing Berlin Jakarta Lagos Mexico City Average values Kappa Overall Accuracy Std. Dev. Comission Omission KTH - UNIPV 0,707 85,36% 4% 5,47 23,75 GlobCover 0,471 72,67% 13% 17,10 40,47 Modis 500 0,525 76,31% 11% 20,03 31,12 Mumbai New York City Rio de Janeiro Stockholm Sydney Ban., Y., A. Jacob & P. Gamba, Spaceborne SAR Data for Global Urban Mapping at 30m Resolution Using a Robust Urban Extractor. ISPRS J. of Photogrammetry & Remote Sensing Results: Urban Change Detection 16

17 Stockholm: S2 vs. S1 & S2 Fusion Overall accuracy = 82.75% Kappa Coefficient = Urban Green Structure 17

18 Urban Green Structure Changes Urbanization & Environmental Impact: Greater Toronto Area Furberg, D. and Y. Ban, Satellite Monitoring of Urban Sprawl and Assessment of its Potential Environmental Impact in the GTA between 1985 and Environmental Management. 18

19 Urbanization & Environmental Impact: Stockholm vs. Shanghai left column, 2000 in the middle one and 2010 in the right one. Haas, J., Furberg, D., and Ban, Y., Satellite Monitoring of Urbanization and Environmental Impact Assessment: Comparison of Stockholm and Shanghai, International Journal of Applied Earth Observation and Geoinformation. Haas, J., and Y. Ban, Urban growth and environmental impacts in Jing-Jin-Ji, the Yangtze River Delta and the Pearl River Delta International Journal of Applied Earth Observation and Geoinformation. Furberg, D. and Y. Ban, Satellite Monitoring of Urban Land Cover Change in Stockholm between 1986 and 2006 and Environmental Impact Assessment through the Use of Indicators. EOGC 2011, Springer Book. Urban Land Cover Classification & Change Detection Haas, J. and Y. Ban Mapping and Monitoring Urban Ecosystem Services Using High-Resolution Satellite Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 19

20 Environmental Impact Assessment Percentage of landscape changes from 2005 to 2015 Overall Kappa accuracy % 2005 Landsat Sentinel-2A Haas, J. and Y. Ban, Spatio-temporal urban ecosystem service analysis with Sentinel-2A MSI data (submitted to Remote Sensing of Environment). Environmental Impact Assessment Cropland in the ruralurban fringe new high and low density built-up areas, urban green spaces and golf courses. Similar to Shanghai, highrise buildings and green spaces replace old low-rise industrial and commercial complexes. Food supply, noise reduction, waste treatment and global climate regulation most affected. 20

21 ViSuCity: A Visual Sustainable City Planning Tool Visualization Heavy Project Funded by KKS, VINNOVA, SSF etc. Multi-disciplinary partners Academic: Geoinformatics, Urban Planning & Computer Science Industry: Sightline, Sweco, BLOM, DigPro, etc. Public Sector: SBK, RTK, Transportstyrelsen Selected Publications: Ban, Y., P. Jakobsson, L. Kjelldhal, and U. Ranhagen Visualization in ViSuCity, a tool for sustainable city planning. SIGRAD2011. Mao, B. and Y. Ban, Online Visualization of 3D City Model Using CityGML and X3DOM, Cartographica, Vol. 46, No. 2, pp Mao, B., Ban, Y., Harrie, L., A multiple representation data structure for dynamic visualisation of generalised 3D city models. ISPRS Journal of Photogrammetry and Remote Sensing 66 (2), pp Mao, B., Harrie, L., Ban, Y Detection and typification of linear structures for dynamic visualization of 3D city models. Computers, Environment and Urban Systems 36 (3), pp Mao, B. and Y. Ban, Generalization of 3D building texture using image compression and multiple representation data structure. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 79, pp Bo M., Y. Ban and L. Harrie, Real-time visualization of 3D city models at street level based on visual saliency. SCIENCE CHINA Earth Sciences Visulization: Impact of Climate Change 21

22 Modelling Future Urban Growth: Shanghai and Stockholm Scenario-based Modeling in Shanghai Markov chain analysis and cellular automata Modeling LEAM Stockholm Implementation of the Land-Use Evolution & impact Assessment Model for Stockholm Based on Cellular Automata and probability surfaces A B C Rui, Y. and Y. Ban, Exploring the relationship between street centrality and land use in Stockholm. International Journal of Geographic Information Science. Zhang, Q, Y. Ban, J. Liu and Y. Hu Simulation and Analysis of Urban Growth Scenarios for the Greater Shanghai Area, China. Computers, Environment and Urban Systems. Kuang, W., Liu, J., Shao, Q., He, J., Sun, C., Tian, H., Ban, Y., Dynamic urban growth model at regional scale and its application, Acta Geographica Sinica 66 (2), pp Rui, Y. and Y. Ban, Urban Growth modeling with road network expansion and land use development. Advances in Cartography and GIScience: Selection from ICC 2011, Springer Book. Spatial Decision Support Systems for Urban Planning Trajectory mining / prediction Closed Contiguous Frequent Routes Anonymous mobile user presence Trajectory pattern exploration Automatic activity-travel surveys Gidofalvi SDSS for Urban Planning 22

23 Mobility Data Analysis: Taxi Stockholm & GeoLife Beijing Selected Publications: Huang, W., S. Li, X. Liu and Y. Ban Predicting human mobility with activity changes. International Journal of Geographical Information Science Liu, X., Y. Ban and S. Li An across-country comparison of the hierarchical spatial structures of cities, Geomatica, Vol. 68, No. 3 Liu. X and Y. Ban Uncovering Spatio-Temporal Cluster Patterns Using Massive Floating Car Data. ISPRS Int. J. Geo-Information, 2(2), Jia, T. and B. Jiang Exploring Human Activity Patterns Using Taxicab Static Points. ISPRS Int. J. Geo-Information. ITRL GPS Data provider: Scania AB Meta: Format: <id,location,timestamp> 1.2 million records Period: Study area: a corridor between Södertälje and Stockholm KTH ROYAL INSTITUTE OF TECHNOLOGY 29/09/16 46 Web Based Real-time Traffic Visual-analytics: real-time traffic on a network (left) and travel time distribution on selected link (right) 23

24 A tool to assist route improvisation and improve spatial and temporal awareness initially takes destination and due arrival time from user, and continuously reads real-time location from GPS continuously generates/updates graphics indicating outcome of each possible move from current location alarms user when there is no possible move to arrive the destination in time Space Time Alarm Clock A limited version (which works only in Stockholm) is available at Prelipcean, A. C., Schmid, F., and Shirabe, T., 2014, A Space Time Alarm. In: Proceedings of the 11th International Symposium on Location Based Services. Lecture Notes in Geoinformation and Cartography 2015, Berlin: Springer, pp A Marathon Course Design Board 37.4 km 36.8 km Attributes 38.4 km Computer enforces hard constraints Distance 28.2 km Ascent 258 m Descent 22 m Ave. Slope 0.83 % Turns 35 (e.g., Total length = km; Starts and finishes at the Olympic stadium) Designer edits freely with soft constraints (e.g., Pleasant scenery; More (or less) ups and downs; More (or less) turns) Hossain, S. and Shirabe, T., 2012, A Computer-Aided Marathon Course Design Method. In: M.-P. Kwan and N. Xiao (Eds.) Proceedings of the Seventh International Conference on Geographic Information Science, GIScience 2012, Columbus, Ohio, USA, September 18-21, Shirabe, T., 2015, Drawing with Geography. In: Proceedings of the 18th AGILE International Conference on Geographic Information Science. Lecture Notes in Geoinformation and Cartography 2015, Berlin: Springer. In press. 24

25 Decision Support System for Strategic Pleacement of AEDs 25

26 PhD Thesis in Geoinformatics Deliang Xiang, Urban Area Information Extraction From Polarimetric SAR Data, Main Supervisor Jan Haas, Satellite Monitoring of Urbanization and Environmental Impact, Main Supervisor Osama Yousif, Urban Change Detection Using Multitemporal SAR Data, Main Supervisor Lars Skog, Spatial Analysis and Modeling for Health Applications, PhD Thesis. Main Supervisor Yuchu Qin, From LiDAR waveform to Urban Land Cover Map: Modeling, Processing and Application., PhD Thesis. Main Supervisor Yikang Rui, Urban Growth Modeling Based on Land-use Changes and Road Network Expansion, PhD Thesis. Main Supervisor Mohamed El-Mekawy, Unified Building Model for 3D Cities: The Integration of IFC and CityGML. PhD Thesis. Co- Supervisor Xin Niu, Multitemporal Spaceborne Polarimetric SAR Data for Urban Land Cover Mapping, PhD Thesis, Main Supervisor Tao Jia Geospatial Knowledge Discovery using Volunteered Geographic Information: a Complex System Perspective., PhD Thesis, Co-Supervisor Xintao Liu, The Principle of Scaling of Geographic Space and its Application in Urban Studies, PhD Thesis. Co-supervisor Bo Mao, Visualization and Generalization of 3D City Models, PhD Thesis. Main Supervisor. Licentiate Thesis in Geoinformatics Dorothy Furberg, Satellie Monitoring of Urban Growth and Indicator-based Assessment of Environmental Impact Alexander Jacob, Fusion of SAR & Optical Data for Urban Land Cover Classification with KTH-SEG. Main Supervisor Jan Haas, Remote Sensing of Urbanization and Environmental Impacts. Main Supervisor Osama Yousif, Change Detection Using Multitemporal SAR Images. Main Supervisor Xin Niu, Multitemporal Spaceborne Polarimetric SAR Data for Urban Land Cover Mapping. Licentiate Thesis. Main Supervisor Xin Lin, Modern GIR Systems: Framework, Retrieval Model and Indexing Techniques, Licentiate Thesis. Main Supervisor Bo Mao, Visualization and Generalization of 3D City Models, Licentiate Thesis. Qian Zhang, Spatial-Temporal Patterns of Urban Growth and Sprawl: Observation, Analysis and Simulation, Licentiate Thesis. Main Supervisor Hongtao Hu, Urban Land-cover Mapping with High-resolution Spaceborne SAR Data, Licentiate Thesis. Main Supervisor Mohamed El-Mekawy, Integrating BIM and GIS for 3D City Modelling: A Case of IFC and CityGML, Licentiate Thesis. Co- Supervisor Tao Jia, Exploring Massive Volunteered Geographic Information for Geographic Knowledge Discovery, Licentiate Thesis, Co-Supervisor. 26